On-premise machine learning model selection in a network assurance service

Abstract

In one embodiment, a network assurance service uses a first machine-learning based model that is locally deployed to a network to assess a set of input features comprising measurements from the network. The service monitors, locally in the network, performance of the first machine learning-based model. The service determines that the monitored performance of the first machine learning-based model does not meet one or more performance requirements associated with the network. The service selects a second machine learning-based model for deployment to the network, based on the one or more performance requirements associated with the network and on the set of input features of the first machine learning-based model. The service deploys the selected second machine learning-based model to the network as a replacement for the first machine learning-based model.

CPC Classifications